In this project we will make biotechnological protein expression more predictable through the proactive management of cellular translation. Specifically we will develop an experimentally-validated model of global cell translation that can predict tRNA depletion in response to heterologous gene expression. The model’s predictions can be used as a tool to engineer gene sequences for optimal protein expression through the alleviation of translation system imbalance. As well as impacting translations and expression per se, an improved understanding of tRNA and ribosomal trafficking could help develop models that can be used to improve protein folding and solubility. The ability of such models to more predictably design genes that can direct efficient protein translation, folding and solubility will have a significant impact on the biotechnology industry and the people they serve.
Ingenza is a Scottish SME who specialise in the application of industrial biotechnology and synthetic biology, providing efficient scalable bioprocesses to manufacture chemicals, biologics, pharmaceuticals and biofuels, from sustainable sources. Ingenza has a broad and growing customer base across the chemicals, pharmaceuticals, food, feed and fuel industries. Ingenza is led by a management team with over 25 years’ experience in applied bioscience and the development and commercialization of biobased products. In addition to engaging in strategic partnerships to tailor their bioprocess services for clients, Ingenza also license their proprietary bioprocess technologies.
The Academic Partner
The academic partner for this project is the University of Aberdeen through the research expertise of Prof Ian Stansfield and Dr M. Carmen Romano. Prof Stansfield's research interests are centred on protein synthesis, and translational control of gene expression in the yeast model system. His overall contributions to the field have included characterisation the translation termination system, and the experimental validation of mathematical models of ribosome flux and translational control. Dr Romano's research focus is on mathematical modelling of cell and microbial systems. She is a physicist by background and applies methods from statistical physics and nonlinear dynamics to understand different cellular processes, in particular the regulation of gene expression by translation. Together with Prof Stansfield, she has developed a series of models to understand and characterise key mechanisms in translation that influence gene expression regulation.
Reducing the cost of production of valuable proteins, such as enzymes and therapeutics, is a key target for the biotechnology industry. Traditional approaches to increase protein yields are unpredictable as they rely upon empirical and “trial-and-error” methods such as codon bias modification. Through investigating both the theoretical and experimental aspects of protein expression simultaneously this project will build a model which will accelerate the understanding and management of cellular translation. The model will allow improved predictability of protein expression leading to higher protein yields and de-risking of bioprocess development activities.
The broad applicability of this innovative experimentally-validated model system indicates the potential for the technology to have an extremely high commercial value in improving protein expression and fermentation processes across biotechnology.